POS0592 PREDICTING LIVER ENZYME ELEVATION IN PATIENTS WITH RHEUMATOID ARTHRITIS ON TREATMENT WITH METHOTREXATE USING SUPERVISED MACHINE LEARNING
Authors: V. Gilvaz, S. Surendran, M. Cb
Journal: Annals of the Rheumatic Diseases (2022)

Background Methotrexate (MTX) is the most used disease-modifying anti-rheumatic drug (DMARD) in the treatment of rheumatoid arthritis (RA). (1) Chronic use of this medication warrants close laboratory monitoring given its propensity for liver damage and myelosuppression. (2) MTX associated hepatic dysfunction is a well-described adverse effect and has been seen in up to a quarter of patients on long term treatment. (3) Recent advances in health care coupled with the extensive use of electronic health records (EHR) has resulted in the accumulation of large amounts of patient data. Approaches like machine learning (ML) allows us to leverage these large data sets to predict outcomes and support clinical decision making. (4) We hypothesized that a ML model trained on conventionally available EHR data would be able to identify patients at the greatest risk of developing MTX related hepatotoxicity. Objectives To develop a machine learning model to accurately predict liver enzyme elevation in rheumatoid arthritis (RA) patients on treatment with methotrexate (MTX) using electronic health record (EHR) data from a real-world RA cohort. Methods Demographic, clinical, biochemical, as well as prescription information from 569 RA patients were retrospectively collected. Liver transaminase elevation above the upper limit of normal following initiation of MTX, was the primary outcome. The total dataset was randomly split into a training (80%) and test set (20%) and used to develop a Random Forest Classifier model. The best model was selected after hyper-parameter tuning and fivefold cross-validation. Results 104 patients (18.2%) developed hepatic dysfunction while on MTX therapy. The best performing predictive model had an accuracy/F1 score of 0.87. The top ten predictive features were then used to create a limited feature model that retained most of the predictive accuracy, with an accuracy/F1 score of 0.86. Conclusion Our proof-of-concept study suggests the possibility of building a well-performing ML model to predict liver enzyme elevation in RA patients being treated with MTX. Similar ML models could be used to identify ‘high risk’ patients who can then be potentially targeted for closer monitoring with the aim of preventing these adverse outcomes. Conversely, patients at low risk can avoid extra outpatient visits, thereby reducing costs. References [1]OEwierkot J, Szechiñski J. Methotrexate in rheumatoid arthritis. Pharmacological reports Citeseer; 2006;58:473-92. [2]Whittle S, Hughes R. Folate supplementation and methotrexate treatment in rheumatoid arthritis: a review. Rheumatology Oxford University Press; 2004;43:267-71. [3]Mori S, Arima N, Ito M, Ueki Y, Abe Y, Aoyagi K, et al. Incidence, predictive factors, and severity of methotrexate-related liver injury in rheumatoid arthritis: A longitudinal cohort study. Rheumatology Advances in Practice 2020; [4]Panch T, Szolovits P, Atun R. Artificial intelligence, machine learning and health systems. Journal of global health International Society for Global Health; 2018;8. Disclosure of Interests None declared.